Wireless communication systems have an ever-increasing use in transferring voice and data services. Such communication systems include the Global System for Mobile (GSM) communication. However, stray signals, or signals intentionally introduced by frequency reuse methods, can interfere with the proper transmission and reception of voice and data signals and can lower capacity. As a result, a receiver must be capable of processing a signal with interference from at least one channel and extract the desired information sent to the user of interest. It is well known that, for typical cell layouts, the major source of noise experienced by GSM communication devices is due to co-channel or adjacent channel interference. Such noise sources arise from nearby devices transmitting on or near the same channel as the desired signal, or from adjacent channel interference, such as that arising on the desired channel due to spectral leakage for example. Additionally, even in the case no other signal interference is present, the received signal may consist of multiple copies of the transmitted data sequence for example due to multipath channel conditions. This effect is sometimes referred to as self-interference.
Traditionally, the effects of multipath channels are compensated either through the use of Maximum Likelihood Sequence Estimation (MLSE) which is usually implemented using the Viterbi algorithm, or through filtering techniques. In the filtering approach, a desired signal symbol sequence can be estimated by adjusting the filter parameters. Classically, the filter parameters can be determined using the modulated symbol and training sequences contained within the desired signal using well known techniques, such as the Minimum Mean Square Error Block Linear Equalizer (MMSE-BLE) for example, which operates on the complex values of the signal and generally can be implemented in the frequency and time domains.
Traditionally, interference cancellation techniques have focused on adjacent channel suppression by using several filtering operations to suppress the frequencies of the received signal that are not also occupied by the desired signal. Correspondingly, co-channel interference techniques have been proposed, such as joint demodulation, which generally require joint channel estimation methods such as per-survivor-processing, as is known in the art. Joint channel estimation provides a joint determination of impulse responses of co-channel signals and may be based on methods such as per-survivor-processing, as is known in the art. Given a known training sequence, all the co-channel interferers can be estimated jointly. However, this requires a large amount of processing power which constrains the number of equalization parameters than can be used efficiently. Moreover, classical joint demodulation only addresses one co-channel interferer, and does not address adjacent channel interference.
Multiple antenna techniques have also been proposed but these can be complex in their terms of hardware implementation and are therefore are mainly more suited to a base station application. Unfortunately, all of the above techniques are non-trivial in either implementation and/or complexity.
There is a need therefore for improved signal detection in an interference-limited environment. In particular, it would be advantageous to provide linear equalization of the signal to reduce interference from both co-channel and adjacent channel noise sources. It would also be of benefit to provide a low-complexity interference solution using existing hardware while reducing the required processor resources.